Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms
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ABSTRACT:
SUBMITTER: Luo L
PROVIDER: S-EPMC10817126 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.MethodsThree NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.ResultsA total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.ConclusionsAs IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.
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Project description:Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple systems. Its recurrent episodes and fluctuating disease courses have a severe impact on patients. Biomarkers to predict disease prognosis and remission are still lacking in SLE. We downloaded the GSE50772 dataset from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) between SLE and healthy controls. Weighted gene co-expression network analysis was used to identify key gene modules and corresponding genes in SLE. The overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. These key genes were analyzed using 3 machine learning algorithms, including the least absolute shrinkage and selection operator, support vector machine recursive elimination, and random forest algorithms. The overlapped genes were obtained as potential biomarkers for further analysis, investigating and validating the potential biomarkers' possible functions, regulatory mechanisms, diagnostic value, and expression levels. And finally studied the differences between groups in level of immune cell infiltration and explored the relationship between potential biomarkers and immunity. A total of 234 overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. After taking the intersection of the key genes obtained by 3 algorithms, we got 4 potential biomarkers (ARID2, CYSTM1, DDIT3, and RNASE1) with high diagnostic values. Finally, further immune infiltration analysis showed differences in various immune cells in the SLE and healthy control samples. ARID2, CYSTM1, DDIT3, and RNASE1 can affect the immune function of SLE patients. ARID2, CYSTM1, DDIT3, and RNASE1 could be used as immune-related potential biomarkers and therapeutic or diagnostic targets for further research.
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Project description:BackgroundBreast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis.MethodsWe analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers.ResultsWe identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone.ConclusionsOur study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings.
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Project description:BackgroundCircular RNAs (circRNAs) are emerging noncoding RNAs that are involved in many biological processes and diseases. The expression profile of circRNAs in preterm neonates with bronchopulmonary dysplasia (BPD) remains unresolved.MethodsIn BPD infants, peripheral venous blood was drawn and circRNAs were extracted and sequenced by next-generation sequencing. The levels of the selected circRNAs were measured by real-time quantitative reverse transcription PCR.ResultsAmong thousands of circRNAs, 491 circRNAs were significantly changed. Among the top 10 changed circRNAs, hsa_circ_0003122, hsa_circ_0003357, hsa_circ_0009983, hsa_circ_0003037, and hsa_circ_0009256 were significantly increased, while hsa_circ_0014932, hsa_circ_0015109, hsa_circ_0017811, hsa_circ_0020588, and hsa_circ_0015066 were significantly decreased. These altered circRNAs are involved in complicated biological functions and signaling pathways. Additionally, hsa_circ_0005577 (hsa_circ_FANCL), which was significantly increased in the moderate-to-severe BPD subjects, was correlated with oxygenation therapy.ConclusionThese results suggest that an aberrant circRNA profile in the peripheral blood of BPD infants might be important in BPD pathogenesis.
| S-EPMC7370752 | biostudies-literature
Project description:Schizophrenia is a group of severe neurodevelopmental disorders. Identification of peripheral diagnostic biomarkers is an effective approach to improving diagnosis of schizophrenia. In this study, four datasets of schizophrenia patients' blood or serum samples were downloaded from the GEO database and merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WCGNA). The WGCNA analysis showed that the cyan module, among 9 modules, was significantly related to schizophrenia, which subsequently yielded 317 schizophrenia-related key genes by comparing with the DEGs. The enrichment analyses on these key genes indicated a strong correlation with immune-related processes. The CIBERSORT algorithm was adopted to analyze immune cell infiltration, which revealed differences in eosinophils, M0 macrophages, resting mast cells, and gamma delta T cells. Furthermore, by comparing with the immune genes obtained from online databases, 95 immune-related key genes for schizophrenia were screened out. Moreover, machine learning algorithms including Random Forest, LASSO, and SVM-RFE were used to further screen immune-related hub genes of schizophrenia. Finally, CLIC3 was found as an immune-related hub gene of schizophrenia by the three machine learning algorithms. A schizophrenia rat model was established to validate CLIC3 expression and found that CLIC3 levels were reduced in the model rat plasma and brains in a brain-regional dependent manner, but can be reversed by an antipsychotic drug risperidone. In conclusion, using various bioinformatic and biological methods, this study found an immune-related hub gene of schizophrenia - CLIC3 that might be a potential diagnostic biomarker and therapeutic target for schizophrenia.
| S-EPMC10568181 | biostudies-literature
Project description:ObjectivesThis study aimed to analyze the predictive value of umbilical cord blood Interleukin-6 (UCB IL-6) for the severity-graded BPD and to establish machine learning (ML) predictive models in a Chinese population based on the 2019 NRN evidence-based guidelines.MethodsIn this retrospective analysis, we included infants born with gestational age <32 weeks, who underwent UCB IL-6 testing within 24 h of admission to our NICU between 2020 and 2022. We collected their medical information encompassing the maternal, perinatal, and early neonatal phases. Furthermore, we classified the grade of BPD according to the 2019 NRN evidence-based guidelines. The correlation between UCB IL-6 and the grades of BPD was analyzed. Univariate analysis and ordinal logistic regression were employed to identify risk factors, followed by the development of ML predictive models based on XGBoost, CatBoost, LightGBM, and Random Forest. The AUROC was used to evaluate the diagnostic value of each model. Besides, we generated feature importance distribution plots based on SHAP values to emphasize the significance of UCB IL-6 in the models.ResultsThe study ultimately enrolled 414 preterm infants, with No BPD group (n = 309), Grade 1 BPD group (n = 73), and Grade 2-3 BPD group (n = 32). The levels of UCB IL-6 increased with the grades of BPD. UCB IL-6 demonstrated clinical significance in predicting various grades of BPD, particularly in distinguishing Grade 2-3 BPD patients, with an AUROC of 0.815 (95% CI: 0.753-0.877). All four ML models, XGBoost, CatBoost, LightGBM, and Random Forest, exhibited Micro-average AUROC values of 0.841, 0.870, 0.851, and 0.878, respectively. Notably, UCB IL-6 consistently appeared as the most prominent feature across the feature importance distribution plots in all four models.ConclusionUCB IL-6 significantly contributes to predicting severity-graded BPD, especially in grade 2-3 BPD. Through the development of four ML predictive models, we highlighted UCB IL-6's importance.
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Project description:BackgroundThe genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understood thoroughly; there is a lack of specific diagnostic markers and treatment methods for the disease. Hence, we aimed to identify the mechanisms of action at the molecular level and potential molecular markers for this disease.MethodsGene expression profiles of IDCM-HF and non-heart failure (NF) specimens were acquired from the database of Gene Expression Omnibus (GEO). We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using "Metascape". Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further confirmed the differential expression in the IDCM-HF and NF groups using an external database.ResultsWe detected 490 genes exhibiting differential expression between IDCM-HF and NF specimens from the GSE57338 dataset, with most of them being concentrated in the extracellular matrix (ECM) of cells related to biological processes and pathways. After screening, 13 candidate genes were identified. Aquaporin 3 (AQP3) and cytochrome P450 2J2 (CYP2J2) showed high diagnostic efficacy in the GSE57338 and GSE6406 datasets, respectively. In comparison to the NF group, AQP3 was significantly down-regulated in the IDCM-HF group, while CYP2J2 was significantly up-regulated.ConclusionAs far as we know, this is the first study that combines WGCNA and machine learning algorithms to screen for potential biomarkers of IDCM-HF. Our findings suggest that AQP3 and CYP2J2 could be used as novel diagnostic markers and treatment targets of IDCM-HF.
| S-EPMC10154102 | biostudies-literature
Project description:ObjectiveTo quantify and compare levels of potential biomarkers in neonates with (i) Bronchopulmonary dysplasia (BPD); (ii) BPD-associated pulmonary hypertension (BPD-PH); (iii) PH without BPD; and (iv) neonates without lung disease at ~36 weeks postmenstrual age.Study designMultiple potential biomarkers were measured in plasma samples of 90 patients using a multi-spot enzyme-linked immunosorbent assay. Statistical tests done included one-way ANOVA to compare levels of biomarkers between different groups.ResultsHigher levels of ICAM-1 were present in infants with BPD and correlated with its severity. Infants with BPD have significantly higher levels of ANG-2 and lower levels of ANG-1. Infants with PH have higher levels of: IL-6, IL-8, IL-10, and TNF-α. Infants with BPD-PH have significantly lower levels of MCP-1 and higher levels of IL-1β than infants with PH without BPD.ConclusionICAM-1 may be used as a specific biomarker for diagnosis of BPD and its severity.
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Project description:PurposeThe authors aimed to develop a Machine-Learning (ML) algorithm that can predict positive blood culture in the neonatal intensive care unit, using complete blood count and C-reactive protein values.MethodsThe study was based on patients' electronic health records at a tertiary neonatal intensive care unit in São Paulo, Brazil. All blood cultures that had paired complete blood count and C-reactive protein measurements taken at the same time were included. To evaluate the machine learning model's performance, the authors used accuracy, Area Under the Receiver Operating Characteristics (AUROC), recall, precision, and F1-score.ResultsThe dataset included 1181 blood cultures with paired complete blood count plus c-reactive protein and 1911 blood cultures with paired complete blood count only. The f1-score ranged from 0.14 to 0.43, recall ranged from 0.08 to 0.59, precision ranged from 0.29 to 1.00, and accuracy ranged from 0.688 to 0.864.ConclusionComplete blood count parameters and C-reactive protein levels cannot be used in ML models to predict bacteremia in newborns.
| S-EPMC9763374 | biostudies-literature
Project description:BackgroundBronchopulmonary dysplasia (BPD) is a severe respiratory disease that primarily affects premature infants, characterized by persistent inflammation and abnormal immune activation. This study aimed to elucidate the immunological mechanisms underlying BPD by integrating single-cell RNA sequencing with T/B cell receptor profiling of peripheral blood mononuclear cells (PBMCs) from preterm infants with BPD, complemented by validation in a murine BPD model.MethodsWe profiled PBMCs from preterm infants diagnosed with BPD and healthy controls, identifying 22 distinct cell clusters corresponding to major immune cell types.ResultsSignificant alterations were observed in myeloid and lymphoid subsets, with neutrophils undergoing metabolic reprogramming toward oxidative phosphorylation. T and B cell subsets exhibited phenotypic and functional changes, with B cells serving as crucial interaction hubs in cell communication networks. Progenitor cell analysis in BPD mouse models revealed specific alterations in hematopoietic stem cells. Analysis of cell-cell communication networks highlighted intricate intercellular interactions in BPD, emphasizing a pivotal role for the BTLA-TNFRSF14 signaling axis in disease pathogenesis. Additionally, pharmacological blockade of BTLA in mouse models alleviated disease severity, suggesting its potential therapeutic effects through modulation of the BTLA-TNFRSF14 pathway.ConclusionThese findings enhance the understanding of the BPD immune microenvironment and lay the foundation for developing targeted immunomodulatory therapies.HighlightsSingle-cell sequencing revealed immune cell profiles in bronchopulmonary dysplasia (BPD). Neutrophils underwent metabolic changes, and B cells were key in immune communication. Targeting B and T lymphocyte attenuator (BTLA)-TNFRSF14 signalling reduced BPD severity in mouse models, suggesting a potential therapy.
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